A connection has recently been drawn between Dynamic Optimization Problems (DOPs) and Reinforcement Learning Problems (RLPs) where they can be seen as subsets of a broader class of Sequential Decision-Making Problems (SDMPs). SDMPs require new decisions on an ongoing basis. Typically the underlying environment changes between decisions. The SDMP view is useful as it allows the unified space to be explored. Solutions can be designed for characteristics of problem instances using algorithms from either community. Little has been done on comparing algorithm performance across these communities, particularly under real-world resource constraints.

We should not let our inability to discern a locus of intelligence lead us to conclude that programmed computers therefore cannot think. For it may be so with man, as with machine, that, when we understand finally the structure and program, the feeling of mystery (and self-approbation) will weaken.